Sight Environment Research Articles

LOS compensation and trusted NLOS recognition assisted WiFi RTT indoor positioning algorithm

In previous studies, ranging errors in the line of sight (LOS) environment were often overlooked, resulting in an increase in positioning errors. In non-line of sight (NLOS) conditions, the measuring distance hardly participates in the location calculation, leading to the loss of useful positioning information. Thus, this paper proposes a WiFi RTT positioning approach based on LOS compensation and trusted NLOS recognition, which improves the performance of RTT positioning by using the compensated LOS distances and trusted NLOS ranges to estimate location. In the proposed approach, NLOS and LOS identification need to be implemented first. A support vector machine algorithm is used to construct a real-time model that recognizes NLOS and LOS distances based on the proposed classification features. Then, the distances under NLOS and LOS environments are evaluated and compensated, respectively, to obtain reliable NLOS ranges and calibrated LOS measurements. A least squares algorithm is utilized to build the LOS distance compensation model, and Bayesian theorem is applied to recognize the trusted NLOS ranges. The compensated LOS distances and credible NLOS ranges are used for estimating the location. The experimental results demonstrate that the proposed algorithm achieves excellent positioning accuracy, with a mean absolute error of 1.082 m. This signifies a substantial enhancement of 53.34% when compared to the least squares algorithm, a remarkable 60.24% improvement over the weighted centroid positioning algorithm, a significant 36.11% progress in comparison to the RTNLIA algorithm, and a noteworthy 32.82% boost relative to the NLRNB approach.

Joint Active-Passive Beamforming and User Association in IRS-Assisted mmWave Cellular Networks

Intelligent reflecting surfaces (IRSs) are a promising technology for future-generation wireless networks by extending coverage region to blind spots and increasing mmWave propagation paths in non-line of sight environments. User association (UA) in dense millimeter wave (mmWave) networks is vital to characterizing connections among base stations (BSs) and mobile users for load balancing, interference management, and maximizing network utility. However, it has yet to be examined thoroughly in a multi-IRS-aided network. This paper presents a new UA scheme that takes cell interference into account for a multi-cell mmWave cellular network aided with multiple IRSs. We formulate a network spectral efficiency maximization problem by jointly optimizing active beamforming (AB) at the BSs, passive beamforming (PB) at the IRSs, and user-BS association with consideration of the impact of IRSs. We then propose a computationally efficient iterative algorithm based on alternating optimization (AO) to resolve this intractable mixed-integer non-convex problem. A fractional programming technique is used to optimize active beamforming at the BSs and passive beamforming at the IRSs, and a penalization method combined with successive convex programming is applied for UA optimization, which is shown to reach the optimal solution. Simulation results show significant performance improvements obtained by the proposed algorithm, providing higher spectral efficiency compared to several benchmark algorithms, while having a low computational complexity.

Simulation of the Effect of Correlated Packet Loss for sUAS Platforms Operating in Non-Line-of-Sight Indoor Environments

The current state of the art in small Unmanned Aerial System (sUAS) testing and evaluation exists mainly in the realm of outdoor flight. Operating small flying sUAS in constrained indoor or subterranean environments places different constraints on their communication links (control links and camera/sensor links). Communication loss in these environments is much more severe due to the proximity of obstacles. This paper examines how correlated packet loss (burst errors) occurring on both the control and camera communication links affects the ability of pilots to fly and navigate small sUAS platforms in constrained Non-Line of Sight (NLOS) environments. A software test bench called AirSim, a UAV simulator, allows us to better understand the effects of correlated packet loss on flyability without damaging multiple sUAS units by flight testing. The simulation was designed to support the design of test methodologies for evaluating the robustness of the communication links and to understand performance without damaging flight tests. Throughout the simulations, it is observed how different levels of packet loss affect the pilot and the number of simulated crashes into the obstacles placed through space. The simulations modeled packet loss both on the video link and the control link to display how packet loss affects ability to pilot and control the sUAS. The utility of using a simulated environment rather than flight testing prevents damage to the fragile and expensive drones being used.

Performance Analysis and Simulation of Millimeter Wave Cell-Free mMIMO Networks

Towards the 5Gnetworks and beyond, there are a lot of emerging technologies. These technologies include but not limited to; multiple input multiple output (MIMO), cell-Free networks, and millimeter wave bands. The cellular MIMO can provide a satisfied performance for users except the shadowed and the cell-edge ones. In order to overcome this disadvantage, the cell-Free networks are deployed. Through applications of distributed access points (APs), the cell-Free networks can provide a ubiquitous coverage for users as whole. Therefore, they can provide a high throughput for users. Moreover, the applications of millimeter wave bands can provide a high bandwidth and hence, a high throughput for users. In other words, the application of the cell-Free technology combined with the millimeter wave bands can extremely enlarge the users’ throughput. This is the motivation of our manuscript. The purpose of this manuscript is to provide mathematical model and simulation for the cell-Free mMIMO network, operating in the millimeter wave bands. The performance metrics can include; the spectral efficiency (SE), bit error rate (BER), and energy efficiency (EE). It is observed that the centralized cooperation among the APs can let users have a satisfied throughput even the system employs the maximal ratio combining (MRC). Furthermore, all cooperation levels, among APs, can perform well even for non-light of sight (NLOS) environment.

Design and Implementation of Remote Controlling System Using GAN in Optical Camera Communication

This article illustrates the controlling mechanism along with data transmission using optical camera communication in both line-of-sight and non-line-of sight (NLOS) environment. The transmitter of the system is mainly composite of microcontroller (Arduino Mega), DHT sensor, two LEDs, and four push button switches. Here, two LEDs are continuously transmitting the control and data signal. The control signal is operated through push button switches. We have used four different control signals that are transmitted from the LEDs and data transmission by collecting data from the surrounding through the temperature and humidity sensor. The entire transmitter operations are processed and controlled through arduino programming in a microcontroller. The COOK modulation technique is used for this system. For data reception, we have used laptop's camera that is controlled through five different exposure times and operated based on the rolling shutter effect of the camera. A logistic regression algorithm is applied to recognize the data transmitting LED from the normalized intensity of the stripe patterns. The receiver is capable to collect data under certain conditions, if the light source is obstructed by an object through deep neural network. Again, Generative Adversial Network (GAN) is used as a decoder to reduce the BER during moving scenario and NLOS conditions. Different activation functions are used in the GAN model to find the optimum solution for the system. However, we have archived the maximum 2.5 m communication distance with 10-4 BER. The receiver program is designed using Python 3.8.

Effects of incremented auditory feedback on remote vehicle operator task performance

Remote vehicle operators (RVO) work in a sensory-deprived environment. A reduction or absence of sensory cueing like auditory feedback, combined with variable workload, has been attributed to a number of remotely piloted aircraft (RPA) accidents. Therefore, this research sought to understand the relationship between workload and dynamic auditory feedback on RVO task performance. Twenty-four participants completed a counterbalanced series of decision-making (spatial orientation accuracy) and perception (spotting accuracy) tasks in an automated beyond visual line of sight environment, under varying workload and auditory volume levels. The management style employed by participants in dealing with the auditory information was also measured and compared with decision-making performance. A relative decline in spatial orientation accuracy was evident when auditory feedback was considered “soft” or “loud” (±10 dBA) compared with a participant-defined comfortable volume level, but contingent on an adequate level of workload experienced concurrently. From an applied perspective, these findings support the inclusion of adaptive auditory systems in future Remotely Piloted Aircraft Systems (RPAS) designs.

Study on the UWB location algorithm in the NLOS environment

At present, people spend most of their time indoors, so it is necessary to study high-precision positioning. Ultra wide band (UWB) can obtain the ranging accuracy with centimeter-level error. However, since the indoor environment is more complex than the outdoor environment, positioning errors tend to be generated during the UWB positioning due to the influence of non-line of sight (NLOS). Therefore, this paper investigates how to identify the NLOS environment and reduce the NLOS error. This paper proposes a method to determine the line of sight (LOS) environment credibility and uses it to determine the NLOS environment. The data with NLOS error is determined according to the standard deviation of the residual between the square of the predicted ranging value and the square of the measured ranging value. Then the data with NLOS error is compensated by complementary filtering. Finally, the compensated measurement data and the optimal estimation of the state at the previous moment are imported into the Kalman filter (KF) to determine the optimal estimation at the current moment.

Simultaneous Localization of Scatterers and Target User Based on Indoor Prior Information in NLOS Environments

In complex indoor environments, there is often no Line of Sight (LOS) path between the transmitter and the receiver due to walls, moving people, and many other obstacles. Traditional localization method based on LOS measurement is no longer available which provides lower localization accuracy in Non-Line of Sight (NLOS) environments. In response to this problem, this paper proposes a joint localization algorithm for scatterers and target user based on indoor prior information in NLOS scenarios. The algorithm makes use of the prior localization information of the access point (AP) and the wall to determine the position of the scatterers and target user simultaneously. Since the established constrained optimization problem is non-convex which is a difference of convex (DC) problem, it is transformed into a convex one based on a Taylor series expansion (TSE) method before it is solved. At the same time, as only a single AP signal can be received normally, the proposed method is then extended to the scenario with only one AP. Simulation and real-world testing results both show that not only the proposed method based on multiple APs, but also the method based on a single AP achieve high positioning accuracy, which are better than the existing positioning methods mentioned in this article as well.

Impact of Antenna Pattern on TOA Based 3D UAV Localization Using a Terrestrial Sensor Network

In this article, we explore the fundamental limits of the 3-dimensional (3D) localization of unmanned aerial vehicles (UAVs) in conjunction with the effects of 3D antenna radiation patterns. Although localization of UAVs has been studied to some extent in the literature, effects of antenna characteristics on 3D localization remains mostly unexplored. To study such effects, we consider a scenario where a fixed number of radio-frequency (RF) sensors equipped with single or multiple dipole antennas are placed at some known locations on the ground, and they derive the time-difference-of-arrival (TDOA) measurements from the time-of-arrival (TOA) data collected for the UAV that is also equipped with a dipole antenna. We then use these measurements to estimate the 3D location of the UAV, and to derive the Cramer-Rao lower bounds (CRLBs) on the localization error for various orientations of the dipole antennas at the transmitter and the receiver. Namely, we consider vertical-vertical (VV), horizontal-horizontal (HH), and vertical-horizontal (VH) radiation patterns in a purely line-of-sight (LoS) environment and a mixed LoS/Non-line-of-sight (NLoS) environment. We show that the localization accuracy changes in a non-monotonic pattern with respect to the UAV altitude and identify the respective critical altitudes for each of the VV, VH and HH orientations. Subsequently, we propose a multi-antenna signal acquisition technique that mitigates the accuracy degradation due to the antenna pattern mismatches, and we derive the localization CRLB for the multi-antenna scenario. Our numerical results characterize achievable localization accuracy for various antenna configurations, UAV heights, and propagation conditions for representative UAV scenarios.

A Majorization-Minimization Algorithm for Hybrid TOA-RSS Based Localization in NLOS Environment

This letter investigates the problem of accurate localization of a target node in wireless sensor networks using time of arrival (TOA) and received signal strength (RSS) measurements in an adverse non-line of sight (NLOS) environment. The proposed methodology works without any requirement of the NLOS path identification or the prior knowledge of NLOS bias distribution. A non-linear weighted least squares (NLWLS) problem is formulated through general approximations on the hybrid data model. The formulated NLWLS problem is solved using a computationally efficient majorization-minimization (MM) algorithm in which the NLWLS objective is iteratively minimized via simple update steps. The proposed MM algorithm is guaranteed to converge to a stationary point of the NLWLS objective. Simulation results and computational complexity analysis validate that the proposed MM algorithm attains fast convergence with lower latency. Moreover, the proposed hybrid localization algorithm outperforms the state-of-art methods in terms of estimation accuracy and computational complexity.

Performance of a Live Multi-Gateway LoRaWAN and Interference Measurement across Indoor and Outdoor Localities

Little work has been reported on the magnitude and impact of interference with the performance of Internet of Things (IoT) applications operated by Long-Range Wide-Area Network (LoRaWAN) in the unlicensed 868 MHz Industrial, Scientific, and Medical (ISM) band. The propagation performance and signal activity measurement of such technologies can give many insights to effectively build long-range wireless communications in a Non-Line of Sight (NLOS) environment. In this paper, the performance of a live multi-gateway in indoor office site in Glasgow city was analysed in 26 days of traffic measurement. The indoor network performances were compared to similar performance measurements from outdoor LoRaWAN test traffic generated across Glasgow Central Business District (CBD) and elsewhere on the same LoRaWAN. The results revealed 99.95% packet transfer success on the first attempt in the indoor site compared to 95.7% at the external site. The analysis shows that interference is attributed to nearly 50 X greater LoRaWAN outdoor packet loss than indoor. The interference measurement results showed a 13.2–97.3% and 4.8–54% probability of interfering signals, respectively, in the mandatory Long-Range (LoRa) uplink and downlink channels, capable of limiting LoRa coverage in some areas.

Improved coverage measurements through machine learning algorithms in a situational aware channel condition for indoor distributed massive MIMO mm-wave system

In massive MIMO (Multiple Input Multiple Output) mm (millimetre) wave system, the channel conditions are measured and analysed for a better placement of reflectors or antennas. In order to increase the coverage area and to reduce interference among users factors such as pathloss and power delay profile are extracted from the Channel Impulse Response (CIR) i.e. from the received signal with respect to transmitter and receiver channel propagation conditions. In a distributed indoor massive MIMO mm-wave system, pathloss and power delay profile are evaluated for Line of Sight (LoS) and Non-Line of Sight (NLoS) environments at frequencies such as 28 and 39 GHz. Based on these factors, a dataset is constructed for 28 GHz. Algorithms such as Support Vector Machine, KNN and Fine Tree are considered. These algorithms are trained with a set of datasets and are tested for performance metrics such as Mean Absolute Error, Correlation Coefficient, Root Mean Squared Error, Relative Absolute Error, Root Relative Squared Error are evaluated. Simulation results show that an accuracy of 94% and 95% using support vector machine, 93.8% and 94.5% accuracy using KNN and an accuracy of 93.2% and 93.8% using fine tree algorithm for pathloss and power delay profile respectively.

Разработка и анализ системы с множеством излучателей и множеством приёмников для исследовательских целей

Systems with multiple input and multiple output (MIMO) play a key role in modern wireless communication devices. MIMO are used in wireless networks to increase channel throughput and transmission reliability. The implementation of the MIMO system will speed up research in the field of improving communication systems. In this article, we present a system-level analysis of a block level analog RF receiver in a MIMO system in accordance with the performance requirements described in the 3GPP Long-Term Evolution standard. An analytical study of the analog receiving part was carried out. The results show that the proposed system architecture satisfies the given requirements with a good margin. Numerical simulation of the operation of the 44 MIMO system in the line-of sight environments was performed. Taking into account the influence of only the noise of the receiver, a curve is obtained that determines the dependence of the error probability on the distance between the transmitter and receiver. Using this curve, it is possible to determine the distance corresponding to a given error probability for given aperture sizes of the transmitting and receiving arrays.

A Beacon-based Approach for RF Source localization in Outdoor NLOS Environment for Search and Rescue Missions

Due to its low implementation cost, the combination of the Received Signal Strength (RSS) with the Angle of Arrival (AOA) measurements is one of the solutions for Radio Frequency (RF) source localization, especially in a Non-Line of Sight (NLOS) environment. It is critical to determine the search space for a person who is lost in rural areas where the mobile network is unavailable due to a lack of Base Tower Stations (BTS) in order to reduce search time. In this paper, we introduce a new beacon-based approach for RF source localization, where the RF signal is received in NLOS after 1-bounce reflection, by combining the information coming from both the RSS-AOA sensors and the beacons, which are used as helpers- that move along a determined path. The proposed approach relies on determining the reflector’s pose first, after which the RF source is localized. The work has been verified in simulation and the Root Mean Square Error (RMSE) is used as a performance metric for RF source localization. Results show that our proposed approach has the lowest RMSE among localization methods mentioned in the literature under the same conditions.
 HIGHLIGHTS
 
 A new beacon-based approach for RF source localization in Non-Line Of Sight (NLOS) condition
 A reflector’s pose is determined based on the signal received from beacons
 The reflector’s pose is used to determine the location of the RF source
 One bounce reflection is considered since the chance of receiving RF signal with more reflections is very low
 
 GRAPHICAL ABSTRACT

Performance optimization for UWB communication network under IEEE 802.15.4a channel conditions

The manuscript presents a performance optimization methodology for UWB signaling under IEEE 802.15.4a channel conditions using pulse shaping. IEEE 802.1.5.4a is widely adapted for wireless personal area network (WPAN), body area network (BAN) as networking standard. The modified pulse shaping is specifically analyzed for the outdoor and industrial IEEE 802.15.4a channel conditions. The pulse shaping is also validated for the effect of interference levels offered to the co-existing Wireless MAN-SCa and Wireless MAN-OFDM (orthogonal frequency-division multiplexing) signaling. The performance has been evaluated for the Gaussian pulse shaping in a multi-user environment. The results for the pulse optimization have been validated for the line of sight and non-line of sight environment to investigate the capability of the UWB pulse for data transmission in multi user scenarios.

A TDOA localization method for complex environment localization

Time Difference of Arrival (TDOA) algorithm is a widely used wireless network-based localization algorithm. However, with the increasing complexity of real-life localization environments, the disturbances of noise, perturbations and multi-path effect have limited the performance of the classical TDOA algorithm. In view of these, we design an Interaction-derived Sequence Selection (IDSS) method and an Interaction-derived Sequence Adjustment (IDSA) method to reduce localization error caused by Non Line of Sight (NLOS) environments. Furthermore, an Interacting Multiple Model (IMM) framework is proposed to deal with the localization problem in complex mixed environments. Experimental results demonstrate that the IDSS and IDSA methods can enhance TDOA localization performance in NLOS environments, and the IMM framework can maintain strong adaptability in complex mixed environments.

Research on Indoor UWB Positioning Based on Expectation Maximization in NLOS Environment

SummaryUltra‐wide band (UWB) technology is especially suitable for indoor positioning because of its strong anti‐interference ability, it can achieve centimeter‐level positioning accuracy in the line of sight (LOS) environment, but it is challenging to construct a robust and high‐precision algorithm in indoor complex and changeable environments. Therefore, it establishes an improved standard Kalman filter (SKF) based on expectation maximization (SKF‐EM), which models the prediction error covariance and measurement noise using the maximum likelihood criterion, estimates the filter parameters online through the expectation maximum theory, and updates the Kalman filter gain according to the Kalman filter based on dropped measurements method (SKF‐DMM). Experimental results show that: in case of serious occlusion, the maximum positioning error of SKF‐EM algorithm is 1.03 m, 39% lower than that of SKF and 60.2% lower than that of UWB.

Sub-Connected Structure Hybrid Precoding for Millimeter-Wave NOMA Communications

Sub-connected structure hybrid beamforming (SCS-HBF) exploiting both successful interference cancellation and zero-forcing (ZF) is tortured by frail performance when deployed for millimeter-Wave non-orthogonal multiple access (NOMA) system in a complete non-line of sight (NLOS) environment. Therefore, this letter articulates a sum-rate maximization for SCS-HBF-NOMA system built based on unique phased-ZF and dynamic power optimization schemes robust for both line of sight and NLOS environments. Radio frequency chain's diversity impact on the proposed scheme's performance is also analyzed.

Robust RSS-Based Source Localization With Unknown Model Parameters in Mixed LOS/NLOS Environments

In this paper, we address the received-signal-strength (RSS) based source localization problem in mixed line-of-sight/non-line-of-sight (LOS/NLOS) environments, where the model parameters are unknown. To accommodate the additional path losses incurred by NLOS signal propagations, we introduce a random variable to the outdoor RSS measurement model to represent the additional path loss. Based on this modified model, we propose a robust localization method for the case of unknown model parameters. Specifically, we introduce a balancing parameter and express the additional path loss term as the sum of the balancing parameter and an error term, and then formulate a robust weighted least squares (RWLS) problem to jointly estimate the source location, the unknown model parameters, and the balancing parameter. The RWLS problem is solved in an iterative manner, where the S-Lemma and the semidefinite relaxation technique are used. Both simulated and real experimental data verify that the proposed method works well in both dense and sparse NLOS environments.

Research on Node localization Algorithm in WSN Based on TDOA

Many applications of wireless sensor networks (WSN) require nodes to know their location information. Monitoring data without node location information is meaningless in many situations. Since the node positioning efficiency of WSN directly affects the performance of the network, designing an efficient positioning algorithm is the focus of WSN research. Based on the principle of TDOA positioning in WSN, it studies two main positioning algorithms: Chan and Taylor algorithm. Meanwhile the least square (LS) method is used to provide the initial position for Taylor algorithm and the first calculation result for Chan algorithm. Simulation results under different conditions show that the Chan algorithm has a good positioning accuracy and less computation than that of Taylor algorithm in the line of sight (LOS) environment. However, under the actual channel, due to the influence of No Line of Sight (NLOS), the positioning accuracy of Taylor algorithm is better than that of Chan algorithm, but its computation is large.